#!/usr/bin/env python
import os, glob, shutil, sys, inspect, errno
from distutils.spawn import find_executable
from mrtrix3 import app, fsl, file, image, path, run
print('Hello')
app.init(
'Lea Vinokur ([email protected])',
'Calculate a fixel-template based on 100 HCP subjects. '
'The analysis pipeline relies primarily on the MRtrix3 software package (www.mrtrix.org).'
)
app.cmdline.add_argument('in_dir',
help='The directory with the input dataset ')
app.cmdline.add_argument(
'output_dir',
help='The directory where the output files '
'should be stored. If you are running group level analysis '
'this folder should be prepopulated with the results of the '
'participant level analysis.')
analysis_level_choices = [
'participant1', 'group1', 'participant2', 'group2', 'participant3',
'group3', 'participant4', 'group4'
]
app.cmdline.add_argument(
'analysis_level',
help='Level of the analysis that will be performed. '
import matlab.engine
import os
PATH = os.environ['PATH'].split(":")
mrtrixbin = [s for s in PATH if "mrtrix3" in s][0]
if not mrtrixbin:
print("cannot find path to mrtrix3, please make sure <path/to/mrtrix3/bin> is in your PATH")
quit()
mrtrixlib = "".join(mrtrixbin)[:-3]+'lib'
print(mrtrixlib)
import inspect, sys, numpy as np, math, gzip, shutil
from distutils.spawn import find_executable
sys.path.insert(0, mrtrixlib)
from mrtrix3 import app, file, fsl, image, path, phaseEncoding, run
app.init('Benjamin Ades-Aron ([email protected])',
'DWI processing with DESIGNER')
app.cmdline.addDescription("""1. pre-check: concatenate all dwi series and make sure the all diffusion AP images and PA image have the same matrix dimensions/orientations
2. denoise the complete dataset\n
3. gibbs ringing correction on complete dataset\n
4. If multiple diffusion series are input, do rigid boy alignment\n
5. topup + eddy, rotate output bvecs\n
6. perform b1 bias correction on each dwi subset\n
7. CSF excluded smoothing\n
8. Rician bias correction\n
for row in mean_RF:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.increment()
with open(os.path.join(output_dir, 'mean_connectome.csv'), 'w') as f:
for row in mean_connectome:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.done()
# End of runGroup() function
analysis_choices = ['participant', 'group']
parcellation_choices = ['aal', 'aal2', 'fs_2005', 'fs_2009']
app.init(
'Robert E. Smith ([email protected])',
'Generate structural connectomes based on diffusion-weighted and T1-weighted image data using state-of-the-art reconstruction tools, particularly those provided in MRtrix3'
)
# If running within a container, erase existing standard options, and fill with only desired options
if is_container:
for option in reversed(app.cmdline._actions):
app.cmdline._handle_conflict_resolve(
None, [(option.option_strings[0], option)])
# app.cmdline._action_groups[2] is "Standard options" that was created earlier
app.cmdline._action_groups[2].add_argument(
'-d',
'--debug',
dest='debug',
action='store_true',
help=
'In the event of encountering an issue with the script, re-run with this flag set to provide more useful information to the developer'
PATH = os.environ['PATH'].split(":")
mrtrixbin = [s for s in PATH if "mrtrix3" in s][0]
if not mrtrixbin:
print(
"cannot find path to mrtrix3, please make sure <path/to/mrtrix3/bin> is in your PATH"
)
quit()
mrtrixlib = "".join(mrtrixbin)[:-3] + 'lib'
print(mrtrixlib)
import inspect, sys, numpy as np, math, gzip, shutil
from distutils.spawn import find_executable
sys.path.insert(0, mrtrixlib)
from mrtrix3 import app, file, fsl, image, path, phaseEncoding, run
app.init('Benjamin Ades-Aron ([email protected])',
'DWI processing with DESIGNER')
app.cmdline.addDescription(
"""1. pre-check: concatenate all dwi series and make sure the all diffusion AP images and PA image have the same matrix dimensions/orientations
2. denoise the complete dataset\n
3. gibbs ringing correction on complete dataset\n
4. If multiple diffusion series are input, do rigid boy alignment\n
5. topup + eddy, rotate output bvecs\n
6. perform b1 bias correction on each dwi subset\n
7. CSF excluded smoothing\n
8. Rician bias correction\n
#!/usr/bin/env python
import os, glob, shutil, sys, inspect, errno, json
from distutils.spawn import find_executable
from mrtrix3 import app, fsl, file, image, path, run
print ('Hello')
app.init ('Lea Vinokur ([email protected])','Receive HCP unprocessed data and output HCP minimally pre-processed data'
'The analysis pipeline relies primarily on the MRtrix3 software package (www.mrtrix.org).')
app.cmdline.add_argument('in_dir', help='The directory with the input dataset, with HCP data compliant hirarchy ')
app.cmdline.add_argument('output_dir', help='The directory where the output files ')
app.cmdline.add_argument('-grad_coeffs', help='Provide the path to the gradient coefficients, if not default ')
app.cmdline.add_argument('-participant_label', help='The label(s) of the participant(s) that should be analyzed. The label '
'corresponds to sub-<participant_label> from the BIDS spec '
'(so it does not include "sub-"). If this parameter is not '
'provided all subjects should be analyzed. Multiple '
'participants can be specified with a space separated list.',
nargs='+')
app.cmdline.add_argument('-group_subset', help='Define a subset of participants to be used when generating the group-average FOD template and response functions. The subset is to be supplied as a comma separate list. Note the subset should be representable of your entire population and not biased towards one particular group. For example in a patient-control comparison, choose equal numbers of patients and controls. Used in group1 and group2 analysis levels.', nargs=1)
app.cmdline.add_argument('-extra_eddy_args', help='generic string of arguments to be passed to the DiffPreprocPipeline_Eddy.sh script'
' and and subsequently to the run_eddy.sh script and finally to the command '
' that actually invokes the eddy binary')
app.parse()
